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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

130
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
130
Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Deep Learning Assisted Localization of Polycystic Kidney on Contrast-Enhanced CT Images.

Djeane Debora Onthoni1, Ting-Wen Sheng2,3, Prasan Kumar Sahoo1,4

  • 1Department of Computer Science and Information Engineering, Chang Gung University, Guishan 33302, Taiwan.

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Summary
This summary is machine-generated.

This study introduces an AI model for automatically locating kidneys in Contrast-enhanced Computed Tomography (CCT) scans for Autosomal Dominant Polycystic Kidney Disease (ADPKD) patients. The model significantly improves the speed and accuracy of kidney detection, aiding in Total Kidney Volume (TKV) analysis.

Keywords:
autosomal dominant polycystic kidney diseasecontrast-enhanced computed tomographydeep learning

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Radiology
  • Nephrology Research

Background:

  • Total Kidney Volume (TKV) measurement is crucial for monitoring Autosomal Dominant Polycystic Kidney Disease (ADPKD) progression.
  • Manual kidney localization and segmentation from medical images (e.g., CCT) are time-consuming and challenging.
  • The unstructured nature of big data in medical imaging necessitates automated solutions.

Purpose of the Study:

  • To develop an Artificial Intelligence (AI) based automatic localization model for kidneys in ADPKD patients.
  • To enhance the efficiency and precision of kidney detection in Contrast-enhanced Computed Tomography (CCT) images.
  • To facilitate accurate Total Kidney Volume (TKV) calculation for improved ADPKD management.

Main Methods:

  • Design of a robust kidney detection model utilizing Contrast-enhanced Computed Tomography (CCT) images.
  • Implementation of image preprocessing techniques and a Single Shot Detector (SSD) Inception V2 Deep Learning (DL) model.
  • Training and evaluation of the model on a dataset of 110 CCT images comprising 10,078 slices.

Main Results:

  • The developed AI model demonstrated superior performance compared to other Deep Learning (DL) detectors.
  • Achieved a mean Average Precision (mAP) of 94% for image-wise testing and 82% for subject-wise testing (IoU threshold = 0.5).
  • The model proved effective in precise and rapid localization and classification of ADPKD kidneys.

Conclusions:

  • The automatic kidney detection model can significantly assist radiologists in ADPKD diagnosis and monitoring.
  • The AI-driven approach streamlines the segmentation process, leading to more accurate TKV calculations.
  • This technology holds promise for improving the clinical workflow and patient outcomes in ADPKD management.